Suchergebnisse

OBJECTIVES: (1) To examine the relation between concentrations of cadmium (Cd) in rice and urinary concentrations of indicators of renal dysfunction and the prevalence of abnormalities in urine in areas polluted by Cd. (2) To establish the maximum allowable concentration of Cd in rice from these findings. METHODS: The target population consisted of 1703 inhabitants (832 men and 871 women) aged over 50 years who consumed home grown rice and had lived in the same hamlet in areas polluted by Cd in the Kakehashi River basin in Ishikawa Prefecture, Japan for at least 30 years. The correlation coefficients between concentrations of Cd in rice and several urinary substances, the prevalence of abnormalities in urine and sex in hamlets polluted by Cd were calculated. Finally, regression analysis was performed for significant indicators to calculate the maximum allowable concentration of Cd in rice based on values in a control group. CONCLUSIONS: Significant correlations between concentration of Cd in rice and concentrations of urinary beta 2-microglobulin, metallothionein, glucose, and aminonitrogen were established. Similarly, there were significant correlations between concentration of Cd in rice and the prevalence of beta 2-microglobulinuria, metallothioneinuria, glucosuria, proteinuria, proteinuria with glucosuria, and aminonitrogenuria. The highest maximum allowable concentration of Cd in rice calculated for these indicators was 0.34 ppm/l and 0.29 ppm/g creatinine. Both values are lower than 0.4 ppm, the tentative limit prescribed by the Japanese government.

Applications of machine learning combined with AI algorithms have propelled unprecedented economic disruptions across diverse fields in industry, military, medicine, finance, and others. With the forecast for even larger impacts, the present economic impact of machine learning is estimated in the trillions of dollars. But as autonomous machines become ubiquitous, recent problems have surfaced. Early on, and again in 2018, Judea Pearl warned AI scientists they must "build machines that make sense of what goes on in their environment," a warning still unheeded that may impede future development. For example, self-driving vehicles often rely on sparse data; self-driving cars have already been involved in fatalities, including a pedestrian; and yet machine learning is unable to explain the contexts within which it operates.